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README.md CHANGED
@@ -2,6 +2,7 @@
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  language: en
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  license: mit
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  ---
 
5
 
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  # Kosmos-2.5
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@@ -16,6 +17,84 @@ Kosmos-2.5 is a multimodal literate model for machine reading of text-intensive
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  ## NOTE:
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  Since this is a generative model, there is a risk of **hallucination** during the generation process, and it **CAN NOT** guarantee the accuracy of all OCR/Markdown results in the images.
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  ## Citation
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@@ -33,4 +112,7 @@ If you find Kosmos-2.5 useful in your research, please cite the following paper:
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  ## License
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  The content of this project itself is licensed under the [MIT](https://github.com/microsoft/unilm/blob/master/kosmos-2.5/LICENSE)
35
 
36
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
 
 
 
 
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  language: en
3
  license: mit
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  ---
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+ # Under testing
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  # Kosmos-2.5
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  ## NOTE:
18
  Since this is a generative model, there is a risk of **hallucination** during the generation process, and it **CAN NOT** guarantee the accuracy of all OCR/Markdown results in the images.
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+ ## Use with transformers:
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+ ```bash
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+ pip install git+https://github.com/tic-top/transformers.git
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+ ```
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+ ```python
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+ from PIL import Image
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+ import requests
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+ import torch
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+ from transformers import AutoProcessor, AutoModelForVision2Seq
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+ import re
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+
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+ repo = "kirp/kosmos2_5"
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+ device = "cuda:0"
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+ dtype = torch.bfloat16
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+ model = AutoModelForVision2Seq.from_pretrained(repo, device_map=device, torch_dtype=dtype)
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+ processor = AutoProcessor.from_pretrained(repo)
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+
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+ url = "https://huggingface.co/kirp/kosmos2_5/resolve/main/receipt_00008.png"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ prompt = "<ocr>" # <md>
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+
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+ inputs = processor(text=prompt, images=image, return_tensors="pt")
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+ height, width = inputs.pop("height"), inputs.pop("width")
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+ raw_width, raw_height = image.size
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+ scale_height = raw_height / height
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+ scale_width = raw_width / width
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+
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+ inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
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+ inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
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+
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+ generated_ids = model.generate(
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+ **inputs,
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+ max_new_tokens=1024,
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+ )
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+ generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
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+
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+ def postprocess(y, scale_height, scale_width):
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+ y = y.replace(prompt, "")
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+ if "<md>" in prompt:
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+ return y
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+ pattern = r"<bbox><x_\d+><y_\d+><x_\d+><y_\d+></bbox>"
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+ bboxs_raw = re.findall(pattern, y)
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+ lines = re.split(pattern, y)[1:]
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+ bboxs = [re.findall(r"\d+", i) for i in bboxs_raw]
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+ bboxs = [[int(j) for j in i] for i in bboxs]
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+ info = ""
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+ for i in range(len(lines)):
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+ box = bboxs[i]
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+ x0, y0, x1, y1 = box
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+ if not (x0 >= x1 or y0 >= y1):
70
+ x0 = int(x0 * scale_width)
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+ y0 = int(y0 * scale_height)
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+ x1 = int(x1 * scale_width)
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+ y1 = int(y1 * scale_height)
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+ info += f"{x0},{y0},{x1},{y0},{x1},{y1},{x0},{y1},{lines[i]}"
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+ return info
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+
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+ output_text = postprocess(generated_text[0], scale_height, scale_width)
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+ print(output_text)
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+ ```
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+ ```text
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+ 55,595,71,595,71,629,55,629,1
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+ 82,595,481,595,481,635,82,635,[REG] BLACK SAKURA
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+ 716,590,841,590,841,629,716,629,45,455
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+ 55,637,71,637,71,672,55,672,1
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+ 82,637,486,637,486,675,82,675,COOKIE DOH SAUCES
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+ 818,632,843,632,843,668,818,668,0
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+ 51,683,71,683,71,719,51,719,1
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+ 82,683,371,683,371,719,82,719,NATA DE COCO
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+ 820,677,845,677,845,713,820,713,0
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+ 32,770,851,770,851,811,32,811,Sub Total 45,455
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+ 28,811,853,811,853,858,28,858,PB1 (10%) 4,545
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+ 28,857,855,857,855,905,28,905,Rounding 0
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+ 24,905,858,905,858,956,24,956,Total 50,000
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+ 17,1096,868,1096,868,1150,17,1150,Card Payment 50,000
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+ ```
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+
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+
98
 
99
  ## Citation
100
 
 
112
  ## License
113
  The content of this project itself is licensed under the [MIT](https://github.com/microsoft/unilm/blob/master/kosmos-2.5/LICENSE)
114
 
115
+ [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
116
+
117
+
118
+
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