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# -*- coding: utf-8 -*-
"""Demo.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Icb8zeoaudyTDOKM1QySNay1cXzltRAp
"""
import gradio as gr
from PIL import Image
import re
import torch
import torch.nn as nn
from warnings import simplefilter
simplefilter('ignore')
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Seting up the model
from transformers import DonutProcessor, VisionEncoderDecoderModel
print('Loading the base model ....')
base_model = VisionEncoderDecoderModel.from_pretrained('Edgar404/donut-shivi-recognition')
base_processor = DonutProcessor.from_pretrained('Edgar404/donut-shivi-recognition')
print('Loading complete')
print('Loading the latence optimized model ....')
optimized_model = VisionEncoderDecoderModel.from_pretrained('Edgar404/donut-shivi-cheques_KD_320')
optimized_processor = DonutProcessor.from_pretrained('Edgar404/donut-shivi-cheques_KD_320')
print('Loading complete')
print('Loading the performance optimized model ....')
performance_model = VisionEncoderDecoderModel.from_pretrained('Edgar404/donut-shivi-cheques_1920')
performance_processor = DonutProcessor.from_pretrained('Edgar404/donut-shivi-cheques_1920')
print('Loading complete')
models = {'baseline': base_model ,
'performance': performance_model ,
'latence': optimized_model}
processor = {'baseline': base_processor ,
'performance': performance_processor ,
'latence': optimized_processor}
# setting
def process_image(image , mode = 'baseline' ):
""" Function that takes an image and perform an OCR using the model DonUT via the task document
parsing
parameters
__________
image : a machine readable image of class PIL or numpy"""
model = models[mode]
processor = processor[mode]
d_type = torch.float32
model.to(device)
model.eval()
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
pixel_values = processor(image, return_tensors="pt").pixel_values
outputs = model.generate(
pixel_values.to(device , dtype = d_type),
decoder_input_ids=decoder_input_ids.to(device),
max_length=model.decoder.config.max_position_embeddings,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
output = processor.token2json(sequence)
return output
def image_classifier(image , mode):
return process_image(image , mode)
examples_list = [['./test_images/test_0.jpg' ,"base"] ,
['./test_images/test_1.jpg','base'],
['./test_images/test_2.jpg' ,"base"],
['./test_images/test_3.jpg','base'],
['./test_images/test_4.jpg','base'],
['./test_images/test_5.jpg' ,"base"],
['./test_images/test_6.jpg' ,"base"],
['./test_images/test_7.jpg','base'],
['./test_images/test_8.jpg','base'],
['./test_images/test_9.jpg','base']
]
demo = gr.Interface(fn=image_classifier, inputs=["image",
gr.Radio(["base" , "optimized"], label="mode")],
outputs="text",
examples = examples_list )
demo.launch(share = True , debug = True) |