DeepStream 7.1 + Fixes + New model output format

utils/export_rtmdet.py

@@ -0,0 +1,150 @@
+import os
+import types
+import onnx
+import torch
+import torch.nn as nn
+
+from mmdet.apis import init_detector
+from projects.easydeploy.model import DeployModel, MMYOLOBackend
+from projects.easydeploy.bbox_code import rtmdet_bbox_decoder as bbox_decoder
+
+
+class DeepStreamOutput(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        boxes = x[0]
+        scores, labels = torch.max(x[1], dim=-1, keepdim=True)
+        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
+
+
+def pred_by_feat_deepstream(self, cls_scores, bbox_preds, objectnesses=None, **kwargs):
+    assert len(cls_scores) == len(bbox_preds)
+    dtype = cls_scores[0].dtype
+    device = cls_scores[0].device
+
+    num_imgs = cls_scores[0].shape[0]
+    featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
+
+    mlvl_priors = self.prior_generate(featmap_sizes, dtype=dtype, device=device)
+
+    flatten_priors = torch.cat(mlvl_priors)
+
+    mlvl_strides = [
+        flatten_priors.new_full(
+            (featmap_size[0] * featmap_size[1] * self.num_base_priors,), stride
+        ) for featmap_size, stride in zip(
+            featmap_sizes, self.featmap_strides
+        )
+    ]
+    flatten_stride = torch.cat(mlvl_strides)
+
+    flatten_cls_scores = [
+        cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.num_classes) for cls_score in cls_scores
+    ]
+    cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
+
+    flatten_bbox_preds = [bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) for bbox_pred in bbox_preds]
+    flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
+
+    if objectnesses is not None:
+        flatten_objectness = [objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1) for objectness in objectnesses]
+        flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
+        cls_scores = cls_scores * (flatten_objectness.unsqueeze(-1))
+
+    scores = cls_scores
+
+    bboxes = bbox_decoder(flatten_priors[None], flatten_bbox_preds, flatten_stride)
+
+    return bboxes, scores
+
+
+def rtmdet_export(weights, config, device):
+    model = init_detector(config, weights, device=device)
+    model.eval()
+    deploy_model = DeployModel(baseModel=model, backend=MMYOLOBackend.ONNXRUNTIME, postprocess_cfg=None)
+    deploy_model.eval()
+    deploy_model.with_postprocess = True
+    deploy_model.prior_generate = model.bbox_head.prior_generator.grid_priors
+    deploy_model.num_base_priors = model.bbox_head.num_base_priors
+    deploy_model.featmap_strides = model.bbox_head.featmap_strides
+    deploy_model.num_classes = model.bbox_head.num_classes
+    deploy_model.pred_by_feat = types.MethodType(pred_by_feat_deepstream, deploy_model)
+    return deploy_model
+
+
+def suppress_warnings():
+    import warnings
+    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
+    warnings.filterwarnings('ignore', category=UserWarning)
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+    warnings.filterwarnings('ignore', category=FutureWarning)
+    warnings.filterwarnings('ignore', category=ResourceWarning)
+
+
+def main(args):
+    suppress_warnings()
+
+    print(f'\nStarting: {args.weights}')
+
+    print('Opening RTMDet model')
+
+    device = torch.device('cpu')
+    model = rtmdet_export(args.weights, args.config, device)
+
+    model = nn.Sequential(model, DeepStreamOutput())
+
+    img_size = args.size * 2 if len(args.size) == 1 else args.size
+
+    onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
+    onnx_output_file = f'{args.weights}.onnx'
+
+    dynamic_axes = {
+        'input': {
+            0: 'batch'
+        },
+        'output': {
+            0: 'batch'
+        }
+    }
+
+    print('Exporting 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=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
+    )
+
+    if args.simplify:
+        print('Simplifying the ONNX model')
+        import onnxslim
+        model_onnx = onnx.load(onnx_output_file)
+        model_onnx = onnxslim.slim(model_onnx)
+        onnx.save(model_onnx, onnx_output_file)
+
+    print(f'Done: {onnx_output_file}\n')
+
+
+def parse_args():
+    import argparse
+    parser = argparse.ArgumentParser(description='DeepStream RTMDet conversion')
+    parser.add_argument('-w', '--weights', required=True, type=str, help='Input weights (.pt) file path (required)')
+    parser.add_argument('-c', '--config', required=True, help='Input config (.py) 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=17, 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 not os.path.isfile(args.config):
+        raise SystemExit('Invalid config 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()
+    main(args)