| """ |
| [Yclip-B] ONNX 独立推理示例（不依赖原 open_clip 包）。 |
| 仅需要：onnxruntime + transformers(tokenizer) + PIL + numpy。 |
| 用法：python inference_onnx_example.py [图片路径] [文本1] [文本2] ... |
| """ |
| import sys, os |
| import numpy as np |
| from PIL import Image |
| from transformers import AutoTokenizer |
|
|
| BASE = os.path.dirname(os.path.abspath(__file__)) |
| IMG_ONNX = os.path.join(BASE, "Yclip-B-image-encoder.onnx") |
| TXT_ONNX = os.path.join(BASE, "Yclip-B-text-encoder.onnx") |
| TOK_DIR = os.path.join(BASE, "Yclip-B-tokenizer") |
|
|
| IMAGE_SIZE = 224 |
| MEAN = np.array([0.5, 0.5, 0.5], dtype=np.float32) |
| STD = np.array([0.5, 0.5, 0.5], dtype=np.float32) |
| CONTEXT_LENGTH = 77 |
|
|
|
|
| def preprocess_image(path): |
| img = Image.open(path).convert("RGB").resize((IMAGE_SIZE, IMAGE_SIZE), Image.BICUBIC) |
| arr = np.asarray(img, dtype=np.float32) / 255.0 |
| arr = (arr - MEAN) / STD |
| arr = arr.transpose(2, 0, 1)[None] |
| return arr.astype(np.float32) |
|
|
|
|
| def tokenize(texts): |
| tok = AutoTokenizer.from_pretrained(TOK_DIR, padding_side="right") |
| enc = tok(texts, padding="max_length", truncation=True, |
| return_tensors="np", return_attention_mask=False, max_length=CONTEXT_LENGTH) |
| return enc["input_ids"].astype(np.int64) |
|
|
|
|
| def softmax(x): |
| e = np.exp(x - x.max()); return e / e.sum() |
|
|
|
|
| def main(): |
| args = sys.argv[1:] |
| |
| img_path = args[0] if args else os.path.join(BASE, "demo.png") |
| texts = args[1:] if len(args) > 1 else ["一只猪", "一个狗", "一只猫"] |
| if not os.path.exists(img_path): |
| print(f"[提示] 未找到示例图 {img_path}，请传入图片路径：python inference_onnx_example.py <图片> <文本1> <文本2> ...") |
| return |
|
|
| import onnxruntime as ort |
| img_sess = ort.InferenceSession(IMG_ONNX, providers=["CPUExecutionProvider"]) |
| txt_sess = ort.InferenceSession(TXT_ONNX, providers=["CPUExecutionProvider"]) |
|
|
| imf = img_sess.run(["image_features"], {"pixel_values": preprocess_image(img_path)})[0] |
| txt_ids = tokenize(texts) |
| rows = [txt_sess.run(["text_features"], {"input_ids": txt_ids[i:i+1]})[0] for i in range(len(texts))] |
| txtf = np.concatenate(rows, axis=0) |
|
|
| imf = imf / np.linalg.norm(imf, axis=-1, keepdims=True) |
| txtf = txtf / np.linalg.norm(txtf, axis=-1, keepdims=True) |
| logits = 100.0 * (imf @ txtf.T).squeeze() |
| probs = softmax(logits) |
|
|
| print(f"图片: {img_path}") |
| for t, p in zip(texts, probs): |
| print(f" {t}: {p:.4f}") |
| print(f"预测: {texts[int(np.argmax(probs))]} (置信度 {probs.max():.4f})") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
