feat: 添加图像推理功能，支持使用单个GPU进行模型推理

zhch/demo_hf.py

@@ -0,0 +1,74 @@
+import os
+if "LOCAL_RANK" not in os.environ:
+    os.environ["LOCAL_RANK"] = "0"
+
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
+from qwen_vl_utils import process_vision_info
+from dots_ocr.utils import dict_promptmode_to_prompt
+
+# 强制使用单个GPU
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+def inference(image_path, prompt, model, processor):
+    # image_path = "demo/demo_image1.jpg"
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path
+                },
+                {"type": "text", "text": prompt}
+            ]
+        }
+    ]
+
+
+    # Preparation for inference
+    text = processor.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+
+    inputs = inputs.to("cuda")
+
+    # Inference: Generation of the output
+    generated_ids = model.generate(**inputs, max_new_tokens=24000)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    print(output_text)
+
+
+
+if __name__ == "__main__":
+    # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+    model_path = "./weights/DotsOCR"
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        attn_implementation="flash_attention_2",
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    processor = AutoProcessor.from_pretrained(model_path,  trust_remote_code=True)
+
+    image_path = "demo/demo_image1.jpg"
+    for prompt_mode, prompt in dict_promptmode_to_prompt.items():
+        print(f"prompt: {prompt}")
+        inference(image_path, prompt, model, processor)
+