Initial commit: DCNv4 custom op mirror setup

- Add enhanced README with project structure and quick start guide
- Initialize repository with DCNv4 CUDA extension (PyTorch module)
- Include classification, detection, and segmentation subdirectories
- Reference upstream OpenGVLab DCNv4 implementation

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

DCNv4_op/scripts/find_best.py

@@ -0,0 +1,61 @@
+import json
+import argparse
+class LineParser:
+    def __init__(self) -> None:
+        self.data = {}
+
+    def parse(self, line):
+        def startswith(line, lst):
+            for ele in lst:
+                if line.startswith(ele):
+                    return True
+            return False
+
+        if not startswith(line, ['1', '2', '3', '4', '5', '6', '7', '8', '9']):
+            return
+
+        eles = line.strip().split()
+        key = eles[0]
+        if key not in self.data:
+            self.data[key] = []
+        
+        self.data[key].append([eles[1], float(eles[2])])
+    
+    def sort(self):
+        for k, v in self.data.items():
+            nv = sorted(v, key=lambda x: x[1])
+            self.data[k] = nv
+    
+    def display_best(self):
+        for k, v in self.data.items():
+            print(f'{k} \t {v[0][0]} \t {v[0][1]:.4f} \t {v[1][0]} \t {v[1][1]:.4f}')
+    
+    def display_best_python(self, output):
+        res = {}
+        def parse(spec):
+            d_stride = int(spec.split('/')[0])
+            thread = int(spec.split('/')[1].split('(')[0])
+            m = int(spec.split('(')[1].split(')')[0])
+            return d_stride, thread, m
+
+        for k, v in self.data.items():
+            res[k] = parse(v[0][0])
+
+        with open(output, "w") as f:
+            json.dump(res, f, indent=4)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', type=str)
+    parser.add_argument('--output', type=str)
+    args = parser.parse_args()
+
+    with open(args.input) as f:
+        lines = f.readlines()
+
+    lineparser = LineParser()
+    for line in lines:
+        lineparser.parse(line)
+    lineparser.sort()
+    lineparser.display_best()
+    lineparser.display_best_python(args.output)