完成了web，ray server，重构了代码

src/client_demo.py

@@ -0,0 +1,16 @@
+import requests
+
+# 服务的 URL
+url = "http://127.0.0.1:9900/predict"
+
+# 替换成你要预测的图像的路径
+img_path = "/home/lhy/code/TeXify/src/7.png"
+
+# 构造请求数据
+data = {"img_path": img_path}
+
+# 发送 POST 请求
+response = requests.post(url, json=data)
+
+# 打印响应
+print(response.text)

src/inference.py

@@ -0,0 +1,40 @@
+import os
+import argparse
+
+from pathlib import Path
+from models.ocr_model.utils.inference import inference
+from models.ocr_model.model.TexTeller import TexTeller
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '-img', 
+        type=str, 
+        required=True,
+        help='path to the input image'
+    )
+    parser.add_argument(
+        '-cuda', 
+        default=False,
+        action='store_true',
+        help='use cuda or not'
+    )
+
+    args = parser.parse_args([
+        '-img', './models/ocr_model/test_img/1.png',
+        '-cuda'
+    ])
+
+    script_dirpath = Path(__file__).resolve().parent
+    os.chdir(script_dirpath)
+
+    model = TexTeller.from_pretrained('./models/ocr_model/model_checkpoint')
+    tokenizer = TexTeller.get_tokenizer('./models/tokenizer/roberta-tokenizer-550K')
+
+    # base = '/home/lhy/code/TeXify/src/models/ocr_model/test_img'
+    # img_path = [base + f'/{i}.png' for i in range(7, 12)]
+    img_path = [args.img]
+
+    res = inference(model, tokenizer, img_path, args.cuda)
+    print(res[0])

src/models/ocr_model/inference.py

@@ -1,34 +0,0 @@
-import torch
-import cv2
-import numpy as np
-
-from transformers import RobertaTokenizerFast, GenerationConfig
-from PIL import Image
-from typing import List
-
-from .model.TexTeller import TexTeller
-from .utils.transforms import inference_transform
-from .utils.helpers import convert2rgb
-from ...globals import MAX_TOKEN_SIZE
-
-
-def inference(model: TexTeller, imgs_path: List[str], tokenizer: RobertaTokenizerFast) -> List[str]:
-    imgs = convert2rgb(imgs_path)
-    imgs = inference_transform(imgs)
-    pixel_values = torch.stack(imgs)
-
-    generate_config = GenerationConfig(
-        max_new_tokens=MAX_TOKEN_SIZE,
-        num_beams=3,
-        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
-
-
-if __name__ == '__main__':
-    inference()

src/models/ocr_model/model/TexTeller.py

@@ -1,6 +1,7 @@
 from PIL import Image
+from pathlib import Path
 
-from ....globals import (
+from models.globals import (
     VOCAB_SIZE,
     OCR_IMG_SIZE,
     OCR_IMG_CHANNELS,
@@ -29,16 +30,18 @@ class TexTeller(VisionEncoderDecoderModel):
     
     @classmethod
     def from_pretrained(cls, model_path: str):
-        return VisionEncoderDecoderModel.from_pretrained(model_path)
+        model_path = Path(model_path).resolve()
+        return VisionEncoderDecoderModel.from_pretrained(str(model_path))
 
     @classmethod
     def get_tokenizer(cls, tokenizer_path: str) -> RobertaTokenizerFast:
-        return RobertaTokenizerFast.from_pretrained(tokenizer_path)
+        tokenizer_path = Path(tokenizer_path).resolve()
+        return RobertaTokenizerFast.from_pretrained(str(tokenizer_path))
 
 
 if __name__ == "__main__":
     # texteller = TexTeller()
-    from ..inference import inference
+    from ..utils.inference import inference
     model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/checkpoint-57500')
     tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
 

src/models/ocr_model/train/train.py

@@ -11,7 +11,7 @@ from .training_args import CONFIG
 from ..model.TexTeller import TexTeller
 from ..utils.functional import tokenize_fn, collate_fn, img_transform_fn
 from ..utils.metrics import bleu_metric
-from ....globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT   
+from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT   
 
 
 def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
@@ -82,10 +82,10 @@ if __name__ == '__main__':
     train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
     collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
     # model = TexTeller()
-    model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/bugy_train_without_random_resize/checkpoint-82000')
+    model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/train_with_random_resize/checkpoint-80000')
 
-    enable_train = True
+    enable_train = False
-    enable_evaluate = False
+    enable_evaluate = True
     if enable_train:
         train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)  
     if enable_evaluate:

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,

src/models/resizer/train/train.py

@@ -10,7 +10,7 @@ from transformers import (
 
 from ..utils import preprocess_fn
 from ..model.Resizer import Resizer
-from ....globals import NUM_CHANNELS, NUM_CLASSES, RESIZER_IMG_SIZE
+from ...globals import NUM_CHANNELS, NUM_CLASSES, RESIZER_IMG_SIZE
 
 
 def train():

src/models/resizer/utils/preprocess.py

@@ -2,7 +2,7 @@ import torch
 from torchvision.transforms import v2
 
 from PIL import Image, ImageChops
-from ....globals import (
+from ...globals import (
     IMAGE_MEAN, IMAGE_STD, 
     LABEL_RATIO,
     RESIZER_IMG_SIZE,

src/models/tokenizer/train/train.py

@@ -1,6 +1,6 @@
 from datasets import load_dataset
 from ...ocr_model.model.TexTeller import TexTeller
-from ....globals import VOCAB_SIZE
+from ...globals import VOCAB_SIZE
 
 
 if __name__ == '__main__':

src/server.py

@@ -0,0 +1,91 @@
+import argparse
+import time
+
+from starlette.requests import Request
+from ray import serve
+from ray.serve.handle import DeploymentHandle
+
+from models.ocr_model.utils.inference import inference
+from models.ocr_model.model.TexTeller import TexTeller
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-ckpt', '--checkpoint_dir', type=str, required=True)
+parser.add_argument('-tknz', '--tokenizer_dir', type=str, required=True)
+
+parser.add_argument('-port', '--server_port', type=int, default=8000)
+parser.add_argument('--num_replicas', type=int, default=1)
+parser.add_argument('--ncpu_per_replica', type=float, default=1.0)
+parser.add_argument('--ngpu_per_replica', type=float, default=0.0)
+
+parser.add_argument('--use_cuda', action='store_true', default=False)
+parser.add_argument('--num_beam', type=int, default=1)
+
+# args = parser.parse_args()
+args = parser.parse_args([
+    '--checkpoint_dir', '/home/lhy/code/TeXify/src/models/ocr_model/model_checkpoint',
+    '--tokenizer_dir', '/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550K',
+
+    '--server_port', '9900',
+    '--num_replicas', '1',
+    '--ncpu_per_replica', '1.0',
+    '--ngpu_per_replica', '0.0',
+
+    # '--use_cuda',
+    '--num_beam', '1'
+])
+
+if args.ngpu_per_replica > 0 and not args.use_cuda:
+    raise ValueError("use_cuda must be True if ngpu_per_replica > 0")
+    
+
+@serve.deployment(
+    num_replicas=args.num_replicas, 
+    ray_actor_options={
+        "num_cpus": args.ncpu_per_replica, 
+        "num_gpus": args.ngpu_per_replica
+    }
+)
+class TexTellerServer:
+    def __init__(
+        self, 
+        checkpoint_path: str, 
+        tokenizer_path: str, 
+        use_cuda: bool = False,
+        num_beam: int = 1
+    ) -> None:
+        self.model = TexTeller.from_pretrained(checkpoint_path)
+        self.tokenizer = TexTeller.get_tokenizer(tokenizer_path)
+        self.use_cuda = use_cuda
+        self.num_beam = num_beam
+
+        self.model = self.model.to('cuda') if use_cuda else self.model
+    
+    def predict(self, image_path: str) -> str:
+        return inference(self.model, self.tokenizer, [image_path], self.use_cuda, self.num_beam)[0]
+
+
+@serve.deployment()
+class Ingress:
+    def __init__(self, texteller_server: DeploymentHandle) -> None:
+        self.texteller_server = texteller_server
+    
+    async def __call__(self, request: Request) -> str:
+        msg = await request.json()
+        img_path: str = msg['img_path']
+        pred = await self.texteller_server.predict.remote(img_path)
+        return pred
+
+
+if __name__ == '__main__':
+    ckpt_dir = args.checkpoint_dir
+    tknz_dir = args.tokenizer_dir
+
+    serve.start(http_options={"port": args.server_port})  # 启动一个Ray集群，端口号为9900
+    texteller_server = TexTellerServer.bind(ckpt_dir, tknz_dir, use_cuda=args.use_cuda, num_beam=args.num_beam)
+    ingress = Ingress.bind(texteller_server)
+
+    ingress_handle = serve.run(ingress, route_prefix="/predict")  
+
+    while True:
+        time.sleep(1)

src/start_web.sh

@@ -0,0 +1,9 @@
+#!/usr/bin/env bash
+set -exu
+
+export CHECKPOINT_DIR=/home/lhy/code/TeXify/src/models/ocr_model/model_checkpoint
+export TOKENIZER_DIR=/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550K
+export USE_CUDA=False
+export NUM_BEAM=3
+
+streamlit run web.py

src/web.py

@@ -1,14 +1,25 @@
-import streamlit as st
+import os
 import io
 import base64
-import requests
+import tempfile
+import streamlit as st
 
 from PIL import Image
+from models.ocr_model.utils.inference import inference
+from models.ocr_model.model.TexTeller import TexTeller
 
-def post_image(server_url, img_rb):
-    response = requests.post(server_url, files={'image': img_rb})
-    return response.text
 
+@st.cache_resource
+def get_model():
+    return TexTeller.from_pretrained(os.environ['CHECKPOINT_DIR'])
+
+@st.cache_resource
+def get_tokenizer():
+    return TexTeller.get_tokenizer(os.environ['TOKENIZER_DIR'])
+
+
+model = get_model()
+tokenizer = get_tokenizer()
 
 #  ============================     pages      =============================== #
 # 使用 Markdown 和 HTML 将标题居中
@@ -41,6 +52,12 @@ uploaded_file = st.file_uploader("",type=['jpg', 'png'])
 if uploaded_file:
     # 打开上传图片
     img = Image.open(uploaded_file)
+
+    # 使用tempfile创建临时目录
+    temp_dir = tempfile.mkdtemp()
+    png_file_path = os.path.join(temp_dir, 'image.png')
+    img.save(png_file_path, 'PNG')
+
     # st.image(uploaded_file, caption=f"Input image ({img.height}✖️{img.width})")
 
     # 将 BytesIO 对象转换为 Base64 编码
@@ -78,10 +95,15 @@ if uploaded_file:
     # 预测
     with st.spinner("Predicting..."):
         # 预测结果
-        server_url = 'http://localhost:8000/'
+        server_url = 'http://localhost:9900/'
         uploaded_file.seek(0)
-        TeXTeller_result = post_image(server_url, uploaded_file)
+        TeXTeller_result = inference(
-        TeXTeller_result = r"\begin{align*}" + '\n' + TeXTeller_result + '\n' + r'\end{align*}'
+            model,
+            tokenizer,
+            [png_file_path],
+            bool(os.environ['USE_CUDA']),
+            int(os.environ['NUM_BEAM'])
+        )[0]
         # tab1, tab2 = st.tabs(["✨TeXTeller✨", "pix2tex:gray[(9.6K⭐)️]"])
         tab1, tab2 = st.tabs(["🔥👁️", "pix2tex:gray[(9.6K⭐)️]"])
         # with st.container(border=True):
@@ -91,4 +113,4 @@ if uploaded_file:
             st.code(TeXTeller_result, language='latex')
             st.success('Done!')
 
-#  ============================     pages      =============================== #
+#  ============================     pages      =============================== #