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import torch
import torch.nn as nn
import transformers
from torch.utils.data import Dataset
from transformers import ViTFeatureExtractor
from io import BytesIO
from base64 import b64decode
from PIL import Image
from accelerate import Accelerator
import base64
from config import get_config
from pathlib import Path
from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.trainers import WordLevelTrainer
from tokenizers.pre_tokenizers import Whitespace
from model import build_transformer
import torch.nn.functional as F
from transformers import GPT2TokenizerFast
import streamlit as st
def process(model,image, tokenizer, device):
image = get_image(image)
model.eval()
with torch.no_grad():
encoder_input = image.unsqueeze(0).to(device) # (b, seq_len)
model_out = greedy_decode(model, encoder_input, None, tokenizer, 196,device)
model_text = tokenizer.decode(model_out.detach().cpu().numpy())
return model_text
# get image prompt
def get_image(image):
# import model
model_id = 'google/vit-base-patch16-224-in21k'
feature_extractor = ViTFeatureExtractor.from_pretrained(
model_id
)
image = Image.open(image)
if image.mode != 'RGB':
image = image.convert('RGB')
enc_input = feature_extractor(
image,
return_tensors='pt'
)
return enc_input['pixel_values'].squeeze(0).squeeze(0).squeeze(0).squeeze(0).squeeze(0)
#get tokenizer
def get_or_build_tokenizer(config):
tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2", unk_token ='[UNK]', bos_token = '[SOS]', eos_token = '[EOS]' , pad_token = '[PAD]')
return tokenizer
def causal_mask(size):
mask = torch.triu(torch.ones((1, size, size)), diagonal=1).type(torch.int)
return mask == 0
# get model
def get_model(config, vocab_tgt_len):
model = build_transformer(vocab_tgt_len, config['seq_len'], d_model=config['d_model'])
return model
# greedy decode
def greedy_decode(model, source, source_mask, tokenizer_tgt, max_len, device):
sos_idx = tokenizer_tgt.convert_tokens_to_ids('[SOS]')
eos_idx = tokenizer_tgt.convert_tokens_to_ids('[EOS]')
# Precompute the encoder output and reuse it for every step
encoder_output = model.encode(source, None)
# Initialize the decoder input with the sos token
decoder_input = torch.empty(1, 1).fill_(sos_idx).long().to(device)
while True:
if decoder_input.size(1) == max_len:
break
# build mask for target
decoder_mask = causal_mask(decoder_input.size(1)).long().to(device)
# calculate output
out = model.decode(encoder_output, source_mask, decoder_input, decoder_mask)
# print(f'out: {out.shape}')
# Get next token probabilities with temperature applied
logits = model.project(out[:, -1])
probabilities = F.softmax(logits, dim=-1)
# Greedily select the next word
next_word = torch.argmax(probabilities, dim=1)
# Append next word
decoder_input = torch.cat([decoder_input, next_word.unsqueeze(0)], dim=1)
if next_word.item() == eos_idx:
break
return decoder_input.squeeze(0)
def image_base64(image):
base64_bytes = base64.b64encode(image_file.read())
base64_string = base64_bytes.decode()
return base64_string
def main():
st.title("Image Captioning with Vision Transformer")
image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if image is not None:
st.image(image, use_column_width=True)
# image_bytes = uploaded_file.getvalue()
# image = image_base64(image_bytes)
# image = get_image(uploaded_file)
with st.empty():
st.write("Processing the image... Please wait.")
accelerator = Accelerator()
device = accelerator.device
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = get_config()
tokenizer = get_or_build_tokenizer(config)
model = get_model(config, len(tokenizer))
model = accelerator.prepare(model)
accelerator.load_state('models/')
text_output = process(model, image, tokenizer, device)
st.write(text_output)
if __name__ == "__main__":
main()
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