DeepStream 7.1 + Fixes + New model output format

utils/export_yoloV9.py

@@ -0,0 +1,145 @@
+import os
+import onnx
+import torch
+import torch.nn as nn
+
+import utils.tal.anchor_generator as _m
+
+
+def _dist2bbox(distance, anchor_points, xywh=False, dim=-1):
+    lt, rb = torch.split(distance, 2, dim)
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    return torch.cat((x1y1, x2y2), dim)
+
+
+_m.dist2bbox.__code__ = _dist2bbox.__code__
+
+
+class DeepStreamOutputDual(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        x = x[1].transpose(1, 2)
+        boxes = x[:, :, :4]
+        scores, labels = torch.max(x[:, :, 4:], dim=-1, keepdim=True)
+        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
+
+
+class DeepStreamOutput(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        boxes = x[:, :, :4]
+        scores, labels = torch.max(x[:, :, 4:], dim=-1, keepdim=True)
+        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
+
+
+def yolov9_export(weights, device, inplace=True, fuse=True):
+    ckpt = torch.load(weights, map_location='cpu')
+    ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()
+    if not hasattr(ckpt, 'stride'):
+        ckpt.stride = torch.tensor([32.])
+    if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
+        ckpt.names = dict(enumerate(ckpt.names))
+    model = ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()
+    for m in model.modules():
+        t = type(m)
+        if t.__name__ in ('Hardswish', 'LeakyReLU', 'ReLU', 'ReLU6', 'SiLU', 'Detect', 'Model'):
+            m.inplace = inplace
+        elif t.__name__ == 'Upsample' and not hasattr(m, 'recompute_scale_factor'):
+            m.recompute_scale_factor = None
+    model.eval()
+    head = 'Detect'
+    for k, m in model.named_modules():
+        if m.__class__.__name__ in ('Detect', 'DDetect', 'DualDetect', 'DualDDetect'):
+            m.inplace = False
+            m.dynamic = False
+            m.export = True
+            head = m.__class__.__name__
+    return model, head
+
+
+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 YOLOv9 model')
+
+    device = torch.device('cpu')
+    model, head = yolov9_export(args.weights, device)
+
+    if len(model.names.keys()) > 0:
+        print('Creating labels.txt file')
+        with open('labels.txt', 'w', encoding='utf-8') as f:
+            for name in model.names.values():
+                f.write(f'{name}\n')
+
+    if head in ('Detect', 'DDetect'):
+        model = nn.Sequential(model, DeepStreamOutput())
+    else:
+        model = nn.Sequential(model, DeepStreamOutputDual())
+
+    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 YOLOv9 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=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 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)