fixing donut

single_eval.py

@@ -1,77 +0,0 @@
-from transformers import DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
-import re
-import torch
-from PIL import Image
-import time
-from fastapi import FastAPI, UploadFile, File
-import io
-import os
-from sys import platform
-
-image_size = [1920, 2560]
-print("Set up config")
-config_vision = VisionEncoderDecoderConfig.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
-config_vision.encoder.image_size = image_size # (height, width)
-config_vision.decoder.max_length = 768
-
-processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2")
-model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v3.2", config=config_vision)
-
-processor.image_processor.size = image_size[::-1] # should be (width, height)
-processor.image_processor.do_align_long_axis = False
-
-# dataset = load_dataset(config.validation_dataset_path, split=config.validation_dataset_split)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.eval()
-model.to(device)
-
-print("Print ipconfig")
-if platform == "linux":
-    os.system("ip r")
-else:
-    os.system("ipconfig")
-
-print("Starting server")
-app = FastAPI()
-
-@app.get("/test")
-async def test():
-    return {"message": "Test"}
-
-@app.post("/process")
-async def process_image(file: UploadFile= File(...)):
-
-    request_object_content = await file.read()
-    input_image = Image.open(io.BytesIO(request_object_content))
-
-    # prepare encoder inputs
-    pixel_values = processor(input_image.convert("RGB"), return_tensors="pt").pixel_values
-    pixel_values = pixel_values.to(device)
-    # prepare decoder inputs
-    task_prompt = "<s_cord-v2>"
-    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
-    decoder_input_ids = decoder_input_ids.to(device)
-
-    print("Start processing")
-    # autoregressively generate sequence
-    start_time = time.time()
-    outputs = model.generate(
-            pixel_values,
-            decoder_input_ids=decoder_input_ids,
-            max_length=model.decoder.config.max_position_embeddings,
-            early_stopping=True,
-            pad_token_id=processor.tokenizer.pad_token_id,
-            eos_token_id=processor.tokenizer.eos_token_id,
-            use_cache=True,
-            num_beams=1,
-            bad_words_ids=[[processor.tokenizer.unk_token_id]],
-            return_dict_in_generate=True,
-        )
-    processing_time = (time.time() - start_time)
-
-    # turn into JSON
-    seq = processor.batch_decode(outputs.sequences)[0]
-    seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
-    seq = re.sub(r"<.*?>", "", seq, count=1).strip()  # remove first task start token
-    seq = processor.token2json(seq)
-    return {"data": seq['text_sequence'], "processing_time": f"{processing_time} seconds"}

train.py

@@ -13,6 +13,8 @@ from utils.donut_dataset import DonutDataset
 from utils.donut_model_pl import DonutModelPLModule
 from utils.callbacks import PushToHubCallback
 import warnings
+from datasets import load_dataset
+
 
 
 
@@ -32,8 +34,12 @@ def main(config, hug_token):
 
     added_tokens = []
 
+    dataset = load_dataset(config.dataset_path, split='train', streaming=True)
+    train_dataset = dataset.skip(100)
+    validation_dataset = dataset.take(100)
+
     train_dataset = DonutDataset(
-                        config.dataset_path, 
+                        train_dataset,
                         processor=processor, 
                         model=model, 
                         max_length=config.max_length,
@@ -45,7 +51,7 @@ def main(config, hug_token):
                     )
 
     val_dataset = DonutDataset(
-                        config.dataset_path, 
+                        validation_dataset,
                         processor=processor, 
                         model=model, 
                         max_length=config.max_length,

utils/donut_dataset.py

@@ -24,7 +24,7 @@ class DonutDataset(Dataset):
 
     def __init__(
         self,
-        dataset_name_or_path: str,
+        dataset: Dataset,
         max_length: int,
         processor: DonutProcessor,
         model: VisionEncoderDecoderModel,
@@ -47,8 +47,8 @@ class DonutDataset(Dataset):
         self.sort_json_key = sort_json_key
         self.added_tokens = added_tokens
 
-        self.dataset = load_dataset(dataset_name_or_path, split=self.split)
-        self.dataset_length = len(self.dataset)
+        self.dataset = dataset
+        self.dataset_length = len(list(self.dataset))
 
         self.gt_token_sequences = []
         for sample in self.dataset: