Upload det02_make_det_dynamic.py with huggingface_hub

det02_make_det_dynamic.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+"""
+det02 — ONNX graph surgery to patch det_500m.onnx for dynamic batch.
+
+Patches 3 things in the graph metadata:
+  1. Input dim[0]: 1 → symbolic 'batch'
+  2. Reshape shape constants starting with 1: that 1 → 0 (copy batch dim from input)
+  3. Output dim[0]: 1 → symbolic 'batch'
+
+Saves the result, then prints declared output shapes and actual runtime shapes for N=1 and N=2.
+"""
+import argparse
+import numpy as np
+import onnx
+from onnx import numpy_helper
+import onnxruntime as ort
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', required=True, help='Path to det_500m.onnx')
+parser.add_argument('--out',   required=True, help='Output path for patched model')
+args = parser.parse_args()
+
+model = onnx.load(args.model)
+
+# ── 1. Patch input batch dim ──────────────────────────────────────────────────
+for inp in model.graph.input:
+    d0 = inp.type.tensor_type.shape.dim[0]
+    d0.ClearField('dim_value')
+    d0.dim_param = 'batch'
+    print(f"  Input '{inp.name}': batch dim → dynamic")
+
+# ── 2. Patch Reshape shape initializers that start with 1 ────────────────────
+reshape_shape_names = set()
+for node in model.graph.node:
+    if node.op_type == 'Reshape':
+        reshape_shape_names.add(node.input[1])
+
+patched = 0
+for init in list(model.graph.initializer):
+    if init.name not in reshape_shape_names:
+        continue
+    arr = numpy_helper.to_array(init).copy()
+    if arr.ndim == 1 and arr[0] == 1:
+        print(f"  Reshape initializer '{init.name}': {arr} → ", end='')
+        arr[0] = 0
+        print(arr)
+        new_t = numpy_helper.from_array(arr, init.name)
+        model.graph.initializer.remove(init)
+        model.graph.initializer.append(new_t)
+        patched += 1
+
+print(f'  Patched {patched} Reshape shape constant(s)')
+
+# ── 3. Patch output batch dims ────────────────────────────────────────────────
+for out in model.graph.output:
+    d0 = out.type.tensor_type.shape.dim[0]
+    d0.ClearField('dim_value')
+    d0.dim_param = 'batch'
+    print(f"  Output '{out.name}': batch dim → dynamic")
+
+# ── Save ──────────────────────────────────────────────────────────────────────
+onnx.checker.check_model(model)
+onnx.save(model, args.out)
+print(f'\nSaved: {args.out}')
+
+# ── Declared output shapes (from patched graph) ───────────────────────────────
+print('\nDeclared output shapes after surgery:')
+for out in model.graph.output:
+    shape = [d.dim_param if d.HasField('dim_param') else d.dim_value
+             for d in out.type.tensor_type.shape.dim]
+    print(f'  {out.name}: {shape}')
+
+# ── Actual output shapes (from runtime with N=1 and N=2) ─────────────────────
+print('\nActual output shapes at runtime:')
+session = ort.InferenceSession(args.out, providers=['CPUExecutionProvider'])
+inp0 = session.get_inputs()[0]
+out_names = [o.name for o in session.get_outputs()]
+
+for N in (1, 2):
+    dummy = np.random.randint(0, 255, (N, 3, 640, 640)).astype(np.float32)
+    try:
+        outs = session.run(out_names, {inp0.name: dummy})
+        print(f'  N={N}:')
+        for name, o in zip(out_names, outs):
+            print(f'    {name}: {list(o.shape)}')
+    except Exception as e:
+        print(f'  N={N}: FAILED — {e}')