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import sys, os, datasets, json
current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)
# jax
import jax
# Main model - ViTGPT2LM
from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
# Vit - as encoder
from transformers import ViTFeatureExtractor
from PIL import Image
import requests
import numpy as np
# GPT2 / GPT2LM - as decoder
from transformers import ViTFeatureExtractor, GPT2Tokenizer
ckpt_no = 5
model_name_or_path = f'./outputs/ckpt_{ckpt_no}/'
flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(model_name_or_path)
vit_model_name = 'google/vit-base-patch16-224-in21k'
feature_extractor = ViTFeatureExtractor.from_pretrained(vit_model_name)
gpt2_model_name = 'asi/gpt-fr-cased-small'
tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
max_length = 32
num_beams = 8
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
@jax.jit
def predict_fn(pixel_values):
return flax_vit_gpt2_lm.generate(pixel_values, **gen_kwargs)
def predict(image):
# batch dim is added automatically
encoder_inputs = feature_extractor(images=image, return_tensors="jax")
pixel_values = encoder_inputs.pixel_values
# generation
generation = predict_fn(pixel_values)
token_ids = np.array(generation.sequences)[0]
caption = tokenizer.decode(token_ids)
return caption, token_ids
if __name__ == '__main__':
from datetime import datetime
split = 'val'
image_id = 322141
p = f'/home/33611/caption/{split}2014/COCO_{split}2014_{str(image_id).zfill(12)}.jpg'
image = Image.open(p)
caption, token_ids = predict(image)
image.close()
print(f'token_ids: {token_ids}')
print(f'caption: {caption}')
ds = datasets.load_dataset('./coco_dataset_script.py', data_dir='/home/33611/caption/')
ds = ds['validation']
ds = ds.select(range(100))
predictions = []
for ex in ds:
p = ex['image_file']
image = Image.open(p)
s = datetime.now()
caption, token_ids = predict(image)
caption = caption.replace('<s>', '').replace('</s>', '').replace('<pad>', '').strip()
image.close()
e = datetime.now()
e = (e - s).total_seconds()
print(f' timing: {e}')
print(f' caption: {ex["fr"]}')
print(f'prediction: {caption}')
print('-' * 20)
ex['pred'] = caption
predictions.append(ex)
with open(f'ckpt_{ckpt_no}_preds.json', 'w', encoding='UTF-8') as fp:
json.dump(predictions, fp, ensure_ascii=False, indent=4)
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