vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

2026-08-22 00:10:33 +08:00
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+# This file is part of OpenCV project.
+# It is subject to the license terms in the LICENSE file found in the top-level directory
+# of this distribution and at http://opencv.org/license.html.
+# Copyright (C) 2026, BigVision LLC, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+'''
+This is a sample script to run Gemma3 inference in OpenCV using ONNX model.
+The script loads the Gemma3 model and runs inference on a given prompt using
+the Gemma3 chat format (<start_of_turn> / <end_of_turn> special tokens).
+
+Model: https://huggingface.co/google/gemma-3-1b-it
+
+Exporting Gemma3 model to ONNX:
+
+1. Install the required dependencies:
+
+    pip install optimum[exporters] optimum-onnx[onnxruntime] torch transformers
+
+2. Export the model to ONNX:
+
+    Without KV-cache:
+
+        optimum-cli export onnx --model google/gemma-3-1b-it --task causal-lm gemma3_instruct_onnx/
+
+    With KV-cache (recommended, faster autoregressive inference):
+
+        optimum-cli export onnx --model google/gemma-3-1b-it --task causal-lm-with-past gemma3_instruct_onnx_with_past/
+
+
+Run the script:
+1. Install the required dependencies:
+
+    pip install numpy
+
+2. Run the script:
+
+    Without KV-cache (causal-lm export):
+
+        python gemma3_inference.py --model=<path-to-onnx-model> \
+                                   --tokenizer_path=<path-to-opencv-tokenizer-config.json> \
+                                   --prompt="What is OpenCV?"
+
+    With KV-cache (causal-lm-with-past export):
+
+        python gemma3_inference.py --model=<path-to-onnx-model> \
+                                   --tokenizer_path=<path-to-opencv-tokenizer-config.json> \
+                                   --prompt="What is OpenCV?" \
+                                   --use_kv_cache
+
+    The tokenizer_path should point to an OpenCV-format config.json (e.g., from
+    opencv_extra/testdata/dnn/llm/gemma3/config.json), NOT the HuggingFace tokenizer_config.json.
+'''
+
+import numpy as np
+import argparse
+import cv2 as cv
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Use this script to run Gemma3 inference in OpenCV',
+                                    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--model', type=str, required=True, help='Path to Gemma3 ONNX model file.')
+    parser.add_argument('--tokenizer_path', type=str, required=True, help='Path to Gemma3 tokenizer config.json.')
+    parser.add_argument('--prompt', type=str, default='What is OpenCV?', help='User prompt.')
+    parser.add_argument('--max_new_tokens', type=int, default=64, help='Maximum number of new tokens to generate.')
+    parser.add_argument('--use_kv_cache', action='store_true', default=False, help='Enable KV-cache for faster inference (requires causal-lm-with-past export).')
+    parser.add_argument('--seed', type=int, default=0, help='Random seed.')
+    return parser.parse_args()
+
+def build_gemma3_prompt(user_prompt):
+    '''Wrap user prompt in Gemma3 chat format.'''
+    return '<start_of_turn>user\n' + user_prompt + '<end_of_turn>\n<start_of_turn>model\n'
+
+def gemma3_inference(net, prompt, max_new_tokens, tokenizer, use_kv_cache=True):
+
+    print("Inferencing Gemma3 model...")
+
+    tokens = tokenizer.encode(prompt)
+    # Prepend BOS token (id=2) as required by Gemma3
+    tokens = [2] + list(tokens)
+    input_ids = np.array(tokens, dtype=np.int64).reshape(1, -1)
+
+    # Gemma3 special token IDs
+    eos_id     = 1    # <eos>
+    eot_id     = 106  # <end_of_turn>
+    stop_ids   = (eos_id, eot_id)
+
+    generated = []
+
+    if use_kv_cache:
+        net.enableKVCache()
+        prompt_len = input_ids.shape[1]
+
+        # Prefill: process full prompt once to populate KV-cache
+        net.setInput(input_ids, 'input_ids')
+        net.setInput(np.ones((1, prompt_len), dtype=np.int64), 'attention_mask')
+        logits = net.forward()
+        new_id = int(np.argmax(logits[:, -1, :].reshape(-1)))
+        generated = [new_id]
+
+        # Generate: feed one new token per step; OpenCV routes present.* -> past_key_values.*
+        for _ in range(max_new_tokens - 1):
+            if new_id in stop_ids:
+                break
+            net.setInput(np.array([[new_id]], dtype=np.int64), 'input_ids')
+            net.setInput(np.ones((1, prompt_len + len(generated)), dtype=np.int64), 'attention_mask')
+            logits = net.forward()
+            new_id = int(np.argmax(logits[:, -1, :].reshape(-1)))
+            generated.append(new_id)
+    else:
+        # Without KV-cache: feed full growing sequence each step
+        for _ in range(max_new_tokens):
+            net.setInput(input_ids, 'input_ids')
+            net.setInput(np.ones((1, input_ids.shape[1]), dtype=np.int64), 'attention_mask')
+            logits = net.forward()
+            new_id = int(np.argmax(logits[:, -1, :].reshape(-1)))
+            if new_id in stop_ids:
+                break
+            generated.append(new_id)
+            input_ids = np.concatenate([input_ids, [[new_id]]], axis=1)
+
+    return np.array([tokens + generated], dtype=np.int64)
+
+if __name__ == '__main__':
+
+    args = parse_args()
+    np.random.seed(args.seed)
+
+    print("Preparing Gemma3 model...")
+    tokenizer = cv.dnn.Tokenizer.load(args.tokenizer_path)
+
+    net = cv.dnn.readNetFromONNX(args.model, cv.dnn.ENGINE_NEW)
+
+    gemma3_prompt = build_gemma3_prompt(args.prompt)
+    print(f"Prompt:\n{gemma3_prompt}")
+
+    prompt_len = len(tokenizer.encode(gemma3_prompt)) + 1  # +1 for BOS token
+    tokens = gemma3_inference(net, gemma3_prompt, args.max_new_tokens, tokenizer, args.use_kv_cache)
+    response = tokenizer.decode(tokens[0][prompt_len:].tolist())
+    print(f"Response:\n{response}")