完成了web，ray server，重构了代码

src/models/ocr_model/utils/helpers.py

@@ -4,12 +4,14 @@ from typing import List
 from PIL import Image
 
 
-def convert2rgb(image_paths: List[str]) -> List[Image.Image]:
+def convert2rgb(image_paths: List[str]) -> List[np.ndarray]:
+    # 输出的np.ndarray的格式为：[H, W, C]（通道在第三维），通道的排列顺序为RGB
     processed_images = []
 
     for path in image_paths:
         # 读取图片
         image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+
         if image is None:
             print(f"Image at {path} could not be read.")
             continue
@@ -32,6 +34,6 @@ def convert2rgb(image_paths: List[str]) -> List[Image.Image]:
         # 如果是 BGR (3通道), 转换为 RGB
         elif channels == 3:
             image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        processed_images.append(Image.fromarray(image))
+        processed_images.append(image)
 
     return processed_images

src/models/ocr_model/utils/inference.py

@@ -0,0 +1,39 @@
+import torch
+
+from transformers import RobertaTokenizerFast, GenerationConfig
+from typing import List
+
+from models.ocr_model.model.TexTeller import TexTeller
+from models.ocr_model.utils.transforms import inference_transform
+from models.ocr_model.utils.helpers import convert2rgb
+from models.globals import MAX_TOKEN_SIZE
+
+
+def inference(
+    model: TexTeller, 
+    tokenizer: RobertaTokenizerFast,
+    imgs_path: List[str], 
+    use_cuda: bool,
+    num_beams: int = 1,
+) -> List[str]:
+    model.eval()
+    imgs = convert2rgb(imgs_path)
+    imgs = inference_transform(imgs)
+    pixel_values = torch.stack(imgs)
+
+    if use_cuda:
+        model = model.to('cuda')
+        pixel_values = pixel_values.to('cuda')
+
+
+    generate_config = GenerationConfig(
+        max_new_tokens=MAX_TOKEN_SIZE,
+        num_beams=num_beams,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+    )
+    pred = model.generate(pixel_values, generation_config=generate_config)
+    res = tokenizer.batch_decode(pred, skip_special_tokens=True)
+    return res

src/models/ocr_model/utils/transforms.py

@@ -7,7 +7,7 @@ from torchvision.transforms import v2
 from typing import List, Union
 from PIL import Image
 
-from ....globals import (
+from ...globals import (
     OCR_IMG_CHANNELS,
     OCR_IMG_SIZE,
     OCR_FIX_SIZE,