Add RT-DETR Paddle

utils/export_rtdetr_paddle.py

@@ -0,0 +1,104 @@
+import os
+import sys
+import warnings
+import onnx
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from ppdet.core.workspace import load_config, merge_config
+from ppdet.utils.check import check_version, check_config
+from ppdet.utils.cli import ArgsParser
+from ppdet.engine import Trainer
+
+
+class DeepStreamOutput(nn.Layer):
+    def __init__(self, img_size, use_focal_loss):
+        self.img_size = img_size
+        self.use_focal_loss = use_focal_loss
+        super().__init__()
+
+    def forward(self, x):
+        boxes = x['bbox']
+        out_shape = paddle.to_tensor([[*self.img_size]]).flip(1).tile([1, 2]).unsqueeze(1)
+        boxes *= out_shape
+        bbox_num = F.sigmoid(x['bbox_num']) if self.use_focal_loss else F.softmax(x['bbox_num'])[:, :, :-1]
+        scores = paddle.max(bbox_num, 2, keepdim=True)
+        classes = paddle.cast(paddle.argmax(bbox_num, 2, keepdim=True), dtype='float32')
+        return boxes, scores, classes
+    
+
+def suppress_warnings():
+    warnings.filterwarnings('ignore')
+
+
+def rtdetr_paddle_export(FLAGS):
+    cfg = load_config(FLAGS.config)
+    FLAGS.opt['weights'] = FLAGS.weights
+    FLAGS.opt['exclude_nms'] = True
+    FLAGS.opt['exclude_post_process'] = True
+    merge_config(FLAGS.opt)
+    merge_config(FLAGS.opt)
+    check_config(cfg)
+    check_version()
+    trainer = Trainer(cfg, mode='test')
+    trainer.load_weights(cfg.weights)
+    trainer.model.eval()
+    if not os.path.exists('.tmp'):
+        os.makedirs('.tmp')
+    static_model, _ = trainer._get_infer_cfg_and_input_spec('.tmp')
+    os.system('rm -r .tmp')
+    return trainer.cfg, static_model
+
+
+def main(FLAGS):
+    suppress_warnings()
+
+    print('\nStarting: %s' % FLAGS.weights)
+
+    print('\nOpening RT-DETR Paddle model\n')
+
+    paddle.set_device('cpu')
+    cfg, model = rtdetr_paddle_export(FLAGS)
+
+    img_size = [cfg.eval_size[1], cfg.eval_size[0]]
+
+    model = nn.Sequential(model, DeepStreamOutput(img_size, cfg.use_focal_loss))
+
+    onnx_input_im = {}
+    onnx_input_im['image'] = paddle.static.InputSpec(shape=[FLAGS.batch, 3, *img_size], dtype='float32', name='image')
+    onnx_output_file = cfg.filename + '.onnx'
+
+    print('\nExporting the model to ONNX\n')
+    paddle.onnx.export(model, cfg.filename, input_spec=[onnx_input_im], opset_version=FLAGS.opset)
+
+    if FLAGS.simplify:
+        print('\nSimplifying 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('\nDone: %s\n' % onnx_output_file)
+
+
+def parse_args():
+    parser = ArgsParser()
+    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pdparams) file path (required)')
+    parser.add_argument('--slim_config', default=None, type=str, help='Slim configuration file of slim method')
+    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('\nInvalid weights file')
+    if args.dynamic and args.batch > 1:
+        raise SystemExit('\nCannot set dynamic batch-size and static batch-size at same time')
+    elif args.dynamic:
+        args.batch = None
+    return args
+
+
+if __name__ == '__main__':
+    FLAGS = parse_args()
+    sys.exit(main(FLAGS))