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inference_demo.py

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+#!/usr/bin/env python3
+"""
+Minimal demo: batched inference with the fixed det_500m model.
+
+Stacks N input images into a single batch and runs one forward pass.
+Prints output shapes and a per-frame summary (count of cls anchors above a
+score threshold per scale). Intended as a sanity check, not a full detector —
+plug your own NMS / anchor decoding for actual face boxes.
+"""
+import argparse
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+DET_SIZE = (640, 640)
+SCORE_THRESHOLD = 0.5
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', required=True, help='Path to det_500m_fixed.onnx')
+parser.add_argument('--images', nargs='+', required=True,
+                    help='One or more image paths (any number — they form the batch)')
+args = parser.parse_args()
+
+
+def preprocess(path):
+    img = cv2.imread(path)
+    if img is None:
+        raise FileNotFoundError(path)
+    blob = cv2.dnn.blobFromImage(
+        cv2.resize(img, DET_SIZE),
+        1.0 / 128.0, DET_SIZE,
+        (127.5, 127.5, 127.5), swapRB=True,
+    )
+    return blob[0]
+
+
+# Build batch
+batch = np.stack([preprocess(p) for p in args.images], axis=0)
+print(f'Input batch: {batch.shape}  ({len(args.images)} image(s))')
+
+# One forward pass
+sess = ort.InferenceSession(args.model, providers=['CPUExecutionProvider'])
+inp_name = sess.get_inputs()[0].name
+out_names = [o.name for o in sess.get_outputs()]
+outputs = sess.run(None, {inp_name: batch})
+
+# Output shapes
+print('\nOutputs:')
+for n, o in zip(out_names, outputs):
+    print(f'  {n}: {list(o.shape)}')
+
+# Per-frame summary using the 3 cls heads (post-Sigmoid, indices 0/1/2)
+strides = [8, 16, 32]
+cls_outputs = outputs[:3]
+
+print(f'\nPer-frame anchor counts above score {SCORE_THRESHOLD}:')
+print(f'  {"image":<40} {"stride 8":>10} {"stride 16":>10} {"stride 32":>10} {"total":>8}')
+for n in range(batch.shape[0]):
+    counts = [int((cls[n] > SCORE_THRESHOLD).sum()) for cls in cls_outputs]
+    name = args.images[n]
+    if len(name) > 38:
+        name = '...' + name[-35:]
+    print(f'  {name:<40} {counts[0]:>10} {counts[1]:>10} {counts[2]:>10} {sum(counts):>8}')
+
+print('\nIf all images are the same, the per-frame counts must match exactly.')