Big update

utils/export_yoloV6.py

@@ -0,0 +1,88 @@
+import os
+import sys
+import argparse
+import warnings
+import onnx
+import torch
+import torch.nn as nn
+from yolov6.utils.checkpoint import load_checkpoint
+from yolov6.layers.common import RepVGGBlock, ConvModule, SiLU
+from yolov6.models.effidehead import Detect
+
+
+class DeepStreamOutput(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        print(x)
+        boxes = x[:, :, :4]
+        objectness = x[:, :, 4:5]
+        scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
+        return torch.cat((boxes, scores, classes, objectness), dim=2)
+
+
+def suppress_warnings():
+    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
+    warnings.filterwarnings('ignore', category=UserWarning)
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+
+
+def yolov6_export(weights, device):
+    model = load_checkpoint(weights, map_location=device, inplace=True, fuse=True)
+    for layer in model.modules():
+        if isinstance(layer, RepVGGBlock):
+            layer.switch_to_deploy()
+        elif isinstance(layer, nn.Upsample) and not hasattr(layer, 'recompute_scale_factor'):
+            layer.recompute_scale_factor = None
+    model.eval()
+    for k, m in model.named_modules():
+        if isinstance(m, ConvModule):
+            if hasattr(m, 'act') and isinstance(m.act, nn.SiLU):
+                m.act = SiLU()
+        elif isinstance(m, Detect):
+            m.inplace = False
+    return model
+
+
+def main(args):
+    suppress_warnings()
+    device = torch.device('cpu')
+    model = yolov6_export(args.weights, device)
+
+    model = nn.Sequential(model, DeepStreamOutput())
+
+    img_size = args.size * 2 if len(args.size) == 1 else args.size
+
+    if img_size == [640, 640] and args.p6:
+        img_size = [1280] * 2
+
+    onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
+    onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.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=None)
+
+    if args.simplify:
+        import onnxsim
+        model_onnx = onnx.load(onnx_output_file)
+        model_onnx, _ = onnxsim.simplify(model_onnx)
+        onnx.save(model_onnx, onnx_output_file)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='DeepStream YOLOv6 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('--p6', action='store_true', help='P6 model')
+    parser.add_argument('--opset', type=int, default=13, help='ONNX opset version')
+    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
+    args = parser.parse_args()
+    if not os.path.isfile(args.weights):
+        raise SystemExit('Invalid weights file')
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    sys.exit(main(args))