Add RT-DETR Ultralytics

utils/export_rtdetr_ultralytics.py

@@ -0,0 +1,124 @@
+import os
+import sys
+import argparse
+import warnings
+import onnx
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ultralytics import RTDETR
+from ultralytics.utils.torch_utils import select_device
+from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
+
+
+class DeepStreamOutput(nn.Module):
+    def __init__(self, img_size):
+        self.img_size = img_size
+        super().__init__()
+
+    def forward(self, x):
+        boxes = x[:, :, :4]
+        boxes[:, :, [0, 2]] *= self.img_size[1]
+        boxes[:, :, [1, 3]] *= self.img_size[0]
+        scores, classes = torch.max(x[:, :, 4:], 2, keepdim=True)
+        classes = classes.float()
+        return boxes, scores, classes
+
+
+def suppress_warnings():
+    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
+    warnings.filterwarnings('ignore', category=UserWarning)
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+
+
+def rtdetr_ultralytics_export(weights, device):
+    model = RTDETR(weights)
+    model = deepcopy(model.model).to(device)
+    for p in model.parameters():
+        p.requires_grad = False
+    model.eval()
+    model.float()
+    model = model.fuse()
+    for k, m in model.named_modules():
+        if isinstance(m, (Detect, RTDETRDecoder)):
+            m.dynamic = False
+            m.export = True
+            m.format = 'onnx'
+        elif isinstance(m, C2f):
+            m.forward = m.forward_split
+    return model
+
+
+def main(args):
+    suppress_warnings()
+
+    print('\nStarting: %s' % args.weights)
+
+    print('Opening RT-DETR Ultralytics model\n')
+
+    device = select_device('cpu')
+    model = rtdetr_ultralytics_export(args.weights, device)
+
+    if len(model.names.keys()) > 0:
+        print('\nCreating labels.txt file')
+        f = open('labels.txt', 'w')
+        for name in model.names.values():
+            f.write(name + '\n')
+        f.close()
+
+    img_size = args.size * 2 if len(args.size) == 1 else args.size
+
+    model = nn.Sequential(model, DeepStreamOutput(img_size))
+
+    onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
+    onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
+
+    dynamic_axes = {
+        'input': {
+            0: 'batch'
+        },
+        'boxes': {
+            0: 'batch'
+        },
+        'scores': {
+            0: 'batch'
+        },
+        'classes': {
+            0: 'batch'
+        }
+    }
+
+    print('\nExporting the model to ONNX')
+    torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
+                      do_constant_folding=True, input_names=['input'], output_names=['boxes', 'scores', 'classes'],
+                      dynamic_axes=dynamic_axes if args.dynamic else None)
+
+    if args.simplify:
+        print('Simplifying the ONNX model')
+        import onnxsim
+        model_onnx = onnx.load(onnx_output_file)
+        model_onnx, _ = onnxsim.simplify(model_onnx)
+        onnx.save(model_onnx, onnx_output_file)
+
+    print('Done: %s\n' % onnx_output_file)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='DeepStream RT-DETR Ultralytics conversion')
+    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
+    parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
+    parser.add_argument('--opset', type=int, default=16, help='ONNX opset version')
+    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
+    parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
+    parser.add_argument('--batch', type=int, default=1, help='Static batch-size')
+    args = parser.parse_args()
+    if not os.path.isfile(args.weights):
+        raise SystemExit('Invalid weights file')
+    if args.dynamic and args.batch > 1:
+        raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    sys.exit(main(args))